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Found 90 papers, 21 papers with code
A Unified Object Counting Network with Object Occupation Prior
In this paper, we build the first evolving object counting dataset and propose a unified object counting network as the first attempt to address this task.
Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview
We believe that the basic theory with mathematical methods, algorithms and simulations of blockchain systems discussed in this paper will strongly support future development and continuous innovation of blockchain technology.
A SVD-based Dynamic Harmonic Phasor Estimator with Improved Suppression of Out-of-Band Interference
The diffusion of nonlinear loads and power electronic devices in power systems deteriorates the signal environment and increases the difficulty of measuring harmonic phasors.
Preserving background sound in noise-robust voice conversion via multi-task learning
Background sound is an informative form of art that is helpful in providing a more immersive experience in real-application voice conversion (VC) scenarios.
Distinguishable Speaker Anonymization based on Formant and Fundamental Frequency Scaling
By directly scaling the formant and F0, the speaker distinguishability degradation of the anonymized speech caused by the introduction of other speakers is prevented.
Deep Learning Based Audio-Visual Multi-Speaker DOA Estimation Using Permutation-Free Loss Function
In this paper, we propose a deep learning based multi-speaker direction of arrival (DOA) estimation with audio and visual signals by using permutation-free loss function.
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge
In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively.
NWPU-ASLP System for the VoicePrivacy 2022 Challenge
Our system consists of four modules, including feature extractor, acoustic model, anonymization module, and neural vocoder.
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize policy performance.
Automatic Comment Generation via Multi-Pass Deliberation
Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process.
Tensor Decomposition based Personalized Federated Learning
Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data.
DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation
This is known as the \emph{Knowledge Tracing} problem in the literature.
Restructuring Graph for Higher Homophily via Learnable Spectral Clustering
While a growing body of literature has been studying new Graph Neural Networks (GNNs) that work on both homophilic and heterophilic graphs, little work has been done on adapting classical GNNs to less-homophilic graphs.
Ranked #18 on
Node Classification
on Squirrel
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Epipolar Focus Spectrum: A Novel Light Field Representation and Application in Dense-view Reconstruction
Existing light field representations, such as epipolar plane image (EPI) and sub-aperture images, do not consider the structural characteristics across the views, so they usually require additional disparity and spatial structure cues for follow-up tasks.
Stereo Unstructured Magnification: Multiple Homography Image for View Synthesis
A two-stage network is developed for novel view synthesis.
CardioID: Mitigating the Effects of Irregular Cardiac Signals for Biometric Identification
In this work, we analyze cardiac signals collected in more realistic (uncontrolled) scenarios and show that their high signal variability (i. e., irregularity) makes it harder to obtain stable and distinct user features.
Sparse Federated Learning with Hierarchical Personalized Models
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users' private data.
MatchFormer: Interleaving Attention in Transformers for Feature Matching
While detector-based methods coupled with feature descriptors struggle in low-texture scenes, CNN-based methods with a sequential extract-to-match pipeline, fail to make use of the matching capacity of the encoder and tend to overburden the decoder for matching.
A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification
In this paper, we propose two techniques, namely joint modeling and data augmentation, to improve system performances for audio-visual scene classification (AVSC).
A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection
In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset.
GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification
Large-scale pre-trained models have brought a new trend to this problem.
Multi Label Text Classification
Multi-Label Text Classification
+1
Knowledge Tracing: A Survey
A human teacher can track the knowledge of students to customize the teaching on students needs.
Fast and Unsupervised Action Boundary Detection for Action Segmentation
To deal with the great number of untrimmed videos produced every day, we propose an efficient unsupervised action segmentation method by detecting boundaries, named action boundary detection (ABD).
Deep Open Set Identification for RF Devices
Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process.
NeurSF: Neural Shading Field for Image Harmonization
In this paper, we decompose the image harmonization into two sub-problems: 1) illumination estimation of background images and 2) rendering of foreground objects.
HDR-NeRF: High Dynamic Range Neural Radiance Fields
The key to our method is to model the physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper.
Learning Data Teaching Strategies Via Knowledge Tracing
In this paper, we propose a novel method, called Knowledge Augmented Data Teaching (KADT), which can optimize a data teaching strategy for a student model by tracing its knowledge progress over multiple learning concepts in a learning task.
A Regularized Wasserstein Framework for Graph Kernels
This framework provides a novel optimal transport distance metric, namely Regularized Wasserstein (RW) discrepancy, which can preserve both features and structure of graphs via Wasserstein distances on features and their local variations, local barycenters and global connectivity.
Multiple Intelligent Reflecting Surface aided Multi-user Weighted Sum-Rate Maximization using Manifold Optimization
Intelligent reflecting surface (IRS) are able to amend radio propagation condition tasks on account of its functional properties in phase shift optimizing.
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"
To elaborate this framework, we propose a novel neural model, called GraphSNN, and prove that this model is strictly more expressive than the Weisfeiler Lehman test in distinguishing graph structures.
Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing
Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states.
VTLayout: Fusion of Visual and Text Features for Document Layout Analysis
In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents.
Structured Directional Pruning via Perturbation Orthogonal Projection
Structured pruning is an effective compression technique to reduce the computation of neural networks, which is usually achieved by adding perturbations to reduce network parameters at the cost of slightly increasing training loss.
Sparse Personalized Federated Learning via Maximizing Correlation
By incorporating an approximated L1-norm and the correlation between client models and global model into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with non-sparse FL.
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC).
The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems
In today's business marketplace, many high-tech Internet enterprises constantly explore innovative ways to provide optimal online user experiences for gaining competitive advantages.
Episode Adaptive Embedding Networks for Few-shot Learning
Few-shot learning aims to learn a classifier using a few labelled instances for each class.
Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction
Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance.
Explore BiLSTM-CRF-Based Models for Open Relation Extraction
Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs
The proposed model is shown to generalize well to both homophilic and heterophilic graphs.
Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet
MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM).
The NPU System for the 2020 Personalized Voice Trigger Challenge
This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge.
FIXME: Enhance Software Reliability with Hybrid Approaches in Cloud
The process of continuous integration/deployment (CICD) in cloud connects developers who need to deliver value faster and more transparently with site reliability engineers (SREs) who need to manage applications reliably.
Machine learning accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.
Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum
Based on these two observations, we propose a learning-based FSS reconstruction approach for one-time aliasing removing over the whole focal stack.
Chiral effective Lagrangian for excited heavy-light mesons from QCD
We derive the chiral effective Lagrangian for excited heavy-light mesons from QCD under proper approximations.
High Energy Physics - Phenomenology
Global Node Attentions via Adaptive Spectral Filters
The proposed model has been shown to generalize well to both assortative and disassortative graphs.
ErGAN: Generative Adversarial Networks for Entity Resolution
Entity resolution targets at identifying records that represent the same real-world entity from one or more datasets.
Chiral effective Lagrangian for heavy-light mesons from QCD: $1/m_{Q}$ correction
The low energy constants in the effective Lagrangian are expressed in terms of the light quark self-energy and heavy quark mass $m_Q$.
High Energy Physics - Phenomenology
Emotion Classification by Jointly Learning to Lexiconize and Classify
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018).
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR.
Frequency Gating: Improved Convolutional Neural Networks for Speech Enhancement in the Time-Frequency Domain
One of the strengths of traditional convolutional neural networks (CNNs) is their inherent translational invariance.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
PEL-BERT: A Joint Model for Protocol Entity Linking
Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently.
Divergence-Augmented Policy Optimization
In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data.
Knowledge Tracing with Sequential Key-Value Memory Networks
Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence.
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets).
Ranked #1 on
Node Classification
on NELL
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning to Sample: an Active Learning Framework
However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning.
Arena: a toolkit for Multi-Agent Reinforcement Learning
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research.
Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction
Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs.
Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition
Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples.
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images
We prove that the light field is a 2D series, thus, a specifically designed CNN-LSTM network is proposed to capture the continuity property of the EPI.
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning
In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings.
Exponentially Weighted Imitation Learning for Batched Historical Data
We consider deep policy learning with only batched historical trajectories.
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely.
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.
A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras
Based on the MPC model and projective transformation, we propose a calibration algorithm to verify our light field camera model.
Dense Light Field Reconstruction From Sparse Sampling Using Residual Network
A light field records numerous light rays from a real-world scene.
End-to-end driving simulation via angle branched network
Imitation learning for end-to-end autonomous driving has drawn attention from academic communities.
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space.
Light Field Segmentation From Super-pixel Graph Representation
The large volume of input data and the redundancy of light field make it an open challenge.
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews
Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities.
Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms
To address these issues, collaborative filtering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation settings, by assuming independent items (i. e., arms).
4D Light Field Superpixel and Segmentation
Superpixel segmentation of 2D image has been widely used in many computer vision tasks.
Collective Vertex Classification Using Recursive Neural Network
In this paper, we propose a graph-based recursive neural network framework for collective vertex classification.
Towards Evidence-Based Ontology for Supporting Systematic Literature Review
[Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.
Discovering and Deciphering Relationships Across Disparate Data Modalities
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.
Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration
The geometry of the recovered scene structure is affected by the calibration of the plenoptic camera significantly.
Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field
Occlusion is one of the most challenging problems in depth estimation.
DARI: Distance metric And Representation Integration for Person Verification
The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately.
Ranked #7 on
Person Re-Identification
on SYSU-30k
(using extra training data)
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars
In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics (i. e. the assigned labels of the target and the references).
Aliasing Detection and Reduction in Plenoptic Imaging
When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts.
